Download checkpoints and labels from Google Drive and put them under the project folder. If you want to use our trained model weights, please also download bitvae_results.
We expect that the data is organized as below.
Before training, please generate a labels/openimages/train.txt according to our provided labels/imagenet/val_example.txt. please replace with the real path on your system.
Before testing, please generate a labels/imagenet/val.txt according to our provided labels/imagenet/val_example.txt. please replace with the real path on your system.
If our work assists your research, feel free to give us a star ⭐ or cite us using:
@misc{Infinity,
title={Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis},
author={Jian Han and Jinlai Liu and Yi Jiang and Bin Yan and Yuqi Zhang and Zehuan Yuan and Bingyue Peng and Xiaobing Liu},
year={2024},
eprint={2412.04431},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2412.04431},
}
@misc{VAR,
title={Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction},
author={Keyu Tian and Yi Jiang and Zehuan Yuan and Bingyue Peng and Liwei Wang},
year={2024},
eprint={2404.02905},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2404.02905},
}
License
This project is licensed under the MIT License - see the LICENSE file for details.
Bitwise Visual Tokenizer
The training and inference code of bitwise tokenizer used by Infinity.
BitVAE Model ZOO
We provide Infinity models for you to play with, which are on
or can be downloaded from the following links:
Visual Tokenizer
Environment installation
Download
checkpointsandlabelsfrom Google Drive and put them under the project folder. If you want to use our trained model weights, please also downloadbitvae_results. We expect that the data is organized as below.Training
Before training, please generate a
labels/openimages/train.txtaccording to our providedlabels/imagenet/val_example.txt. please replace with the real path on your system.Tokenizer with hidden dimension 16
Tokenizer with hidden dimension 32
Testing & evaluation
Before testing, please generate a
labels/imagenet/val.txtaccording to our providedlabels/imagenet/val_example.txt. please replace with the real path on your system.Tokenizer with hidden dimension 16
Tokenizer with hidden dimension 32
📖 Citation
If our work assists your research, feel free to give us a star ⭐ or cite us using:
License
This project is licensed under the MIT License - see the LICENSE file for details.